It is shown in [1]} that under the small variance assumptions, Gibbs sampling can be simplified to DPmeans. Instead of sampling the posterior distribution, \(y_t\)  is assigned to be the maximum likelihood object if the likelihood is within some certain threshold, otherwise it is assigned to a new object.
Intuitively, in this case, small variance means that the noise in odometry, object measurement and object class is relative small, so that the posterior distribution of \(y_t\)  is peaky.
